{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import gymnasium as gym\n",
    "import gym_mtsim\n",
    "\n",
    "from stable_baselines3 import A2C, PPO\n",
    "from stable_baselines3.common.callbacks import BaseCallback\n",
    "\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# env_name = 'forex-hedge-v0'\n",
    "env_name = 'stocks-hedge-v0'\n",
    "# env_name = 'crypto-hedge-v0'\n",
    "# env_name = 'mixed-hedge-v0'\n",
    "\n",
    "# env_name = 'forex-unhedge-v0'\n",
    "# env_name = 'stocks-unhedge-v0'\n",
    "# env_name = 'crypto-unhedge-v0'\n",
    "# env_name = 'mixed-unhedge-v0'\n",
    "\n",
    "env = gym.make(env_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_stats(reward_over_episodes):\n",
    "    \"\"\"  Print Reward  \"\"\"\n",
    "\n",
    "    avg_value = np.mean(reward_over_episodes)\n",
    "    min_value = np.min(reward_over_episodes)\n",
    "    max_value = np.max(reward_over_episodes)\n",
    "\n",
    "    print (f'Min. Reward          : {min_value:>10.3f}')\n",
    "    print (f'Avg. Reward          : {avg_value:>10.3f}')\n",
    "    print (f'Max. Reward          : {max_value:>10.3f}')\n",
    "\n",
    "    return min_value, avg_value, max_value\n",
    "\n",
    "\n",
    "# ProgressBarCallback for model.learn()\n",
    "class ProgressBarCallback(BaseCallback):\n",
    "\n",
    "    def __init__(self, check_freq: int, verbose: int = 1):\n",
    "        super().__init__(verbose)\n",
    "        self.check_freq = check_freq\n",
    "\n",
    "    def _on_training_start(self) -> None:\n",
    "        \"\"\"\n",
    "        This method is called before the first rollout starts.\n",
    "        \"\"\"\n",
    "        self.progress_bar = tqdm(total=self.model._total_timesteps, desc=\"model.learn()\")\n",
    "\n",
    "    def _on_step(self) -> bool:\n",
    "        if self.n_calls % self.check_freq == 0:\n",
    "            self.progress_bar.update(self.check_freq)\n",
    "        return True\n",
    "    \n",
    "    def _on_training_end(self) -> None:\n",
    "        \"\"\"\n",
    "        This event is triggered before exiting the `learn()` method.\n",
    "        \"\"\"\n",
    "        self.progress_bar.close()\n",
    "\n",
    "\n",
    "# TRAINING + TEST\n",
    "def train_test_model(model, env, seed, total_num_episodes, total_learning_timesteps=10_000):\n",
    "    \"\"\" if model=None then execute 'Random actions' \"\"\"\n",
    "\n",
    "    # reproduce training and test\n",
    "    print('-' * 80)\n",
    "    obs = env.reset(seed=seed)\n",
    "    torch.manual_seed(seed)\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "\n",
    "    vec_env = None\n",
    "\n",
    "    if model is not None:\n",
    "        print(f'model {type(model)}')\n",
    "        print(f'policy {type(model.policy)}')\n",
    "        # print(f'model.learn(): {total_learning_timesteps} timesteps ...')\n",
    "\n",
    "        # custom callback for 'progress_bar'\n",
    "        model.learn(total_timesteps=total_learning_timesteps, callback=ProgressBarCallback(100))\n",
    "        # model.learn(total_timesteps=total_learning_timesteps, progress_bar=True)\n",
    "        # ImportError: You must install tqdm and rich in order to use the progress bar callback. \n",
    "        # It is included if you install stable-baselines with the extra packages: `pip install stable-baselines3[extra]`\n",
    "\n",
    "        vec_env = model.get_env()\n",
    "        obs = vec_env.reset()\n",
    "    else:\n",
    "        print (\"RANDOM actions\")\n",
    "\n",
    "    reward_over_episodes = []\n",
    "\n",
    "    tbar = tqdm(range(total_num_episodes))\n",
    "\n",
    "    for episode in tbar:\n",
    "        \n",
    "        if vec_env: \n",
    "            obs = vec_env.reset()\n",
    "        else:\n",
    "            obs, info = env.reset()\n",
    "\n",
    "        total_reward = 0\n",
    "        done = False\n",
    "\n",
    "        while not done:\n",
    "            if model is not None:\n",
    "                action, _states = model.predict(obs)\n",
    "                obs, reward, done, info = vec_env.step(action)\n",
    "            else: # random\n",
    "                action = env.action_space.sample()\n",
    "                obs, reward, terminated, truncated, info = env.step(action)\n",
    "                done = terminated or truncated\n",
    "\n",
    "            total_reward += reward\n",
    "            if done:\n",
    "                break\n",
    "\n",
    "        reward_over_episodes.append(total_reward)\n",
    "\n",
    "        if episode % 10 == 0:\n",
    "            avg_reward = np.mean(reward_over_episodes)\n",
    "            tbar.set_description(f'Episode: {episode}, Avg. Reward: {avg_reward:.3f}')\n",
    "            tbar.update()\n",
    "\n",
    "    tbar.close()\n",
    "    avg_reward = np.mean(reward_over_episodes)\n",
    "\n",
    "    return reward_over_episodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train + Test Env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env_name                 : stocks-hedge-v0\n",
      "seed                     : 2024\n",
      "--------------------------------------------------------------------------------\n",
      "RANDOM actions\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Episode: 40, Avg. Reward: -807.611: 100%|██████████| 50/50 [00:02<00:00, 18.58it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min. Reward          : -10000.000\n",
      "Avg. Reward          :   -351.235\n",
      "Max. Reward          :  21032.950\n",
      "--------------------------------------------------------------------------------\n",
      "model <class 'stable_baselines3.a2c.a2c.A2C'>\n",
      "policy <class 'stable_baselines3.common.policies.MultiInputActorCriticPolicy'>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "model.learn(): 100%|██████████| 25000/25000 [00:55<00:00, 446.53it/s]\n",
      "Episode: 40, Avg. Reward: 157.205: 100%|██████████| 50/50 [00:05<00:00,  9.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min. Reward          :   -231.670\n",
      "Avg. Reward          :    170.335\n",
      "Max. Reward          :    534.650\n",
      "--------------------------------------------------------------------------------\n",
      "model <class 'stable_baselines3.ppo.ppo.PPO'>\n",
      "policy <class 'stable_baselines3.common.policies.MultiInputActorCriticPolicy'>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "model.learn(): 26600it [00:46, 566.55it/s]                           \n",
      "Episode: 40, Avg. Reward: 142.713: 100%|██████████| 50/50 [00:04<00:00, 10.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min. Reward          :   -172.870\n",
      "Avg. Reward          :    141.092\n",
      "Max. Reward          :    600.040\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "seed = 2024  # random seed\n",
    "total_num_episodes = 50\n",
    "\n",
    "print (\"env_name                 :\", env_name)\n",
    "print (\"seed                     :\", seed)\n",
    "\n",
    "# INIT matplotlib\n",
    "plot_settings = {}\n",
    "plot_data = {'x': [i for i in range(1, total_num_episodes + 1)]}\n",
    "\n",
    "# Random actions\n",
    "model = None \n",
    "total_learning_timesteps = 0\n",
    "rewards = train_test_model(model, env, seed, total_num_episodes, total_learning_timesteps)\n",
    "min_value, avg_value, max_value = print_stats(rewards)\n",
    "class_name = f'Random actions'\n",
    "label = f'Avg. {avg_value:>7.2f} : {class_name}'\n",
    "plot_data['rnd_rewards'] = rewards\n",
    "plot_settings['rnd_rewards'] = {'label': label}\n",
    "\n",
    "learning_timesteps_list_in_K = [25]\n",
    "# learning_timesteps_list_in_K = [50, 250, 500]\n",
    "# learning_timesteps_list_in_K = [500, 1000, 3000, 5000]\n",
    "\n",
    "# RL Algorithms: https://stable-baselines3.readthedocs.io/en/master/guide/algos.html\n",
    "model_class_list = [A2C, PPO]\n",
    "\n",
    "for timesteps in learning_timesteps_list_in_K:\n",
    "    total_learning_timesteps = timesteps * 1000\n",
    "    step_key = f'{timesteps}K'\n",
    "\n",
    "    for model_class in model_class_list:\n",
    "        policy_dict = model_class.policy_aliases\n",
    "        # https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html\n",
    "        policy = policy_dict.get('MultiInputPolicy')\n",
    "\n",
    "        try:\n",
    "            model = model_class(policy, env, verbose=0)\n",
    "            class_name = type(model).__qualname__\n",
    "            plot_key = f'{class_name}_rewards_'+step_key\n",
    "            rewards = train_test_model(model, env, seed, total_num_episodes, total_learning_timesteps)\n",
    "            min_value, avg_value, max_value, = print_stats(rewards)\n",
    "            label = f'Avg. {avg_value:>7.2f} : {class_name} - {step_key}'\n",
    "            plot_data[plot_key] = rewards\n",
    "            plot_settings[plot_key] = {'label': label}     \n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"ERROR: {str(e)}\")\n",
    "            continue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame(plot_data)\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "for key in plot_data:\n",
    "    if key == 'x':\n",
    "        continue\n",
    "    label = plot_settings[key]['label']\n",
    "    line = plt.plot('x', key, data=data, linewidth=1, label=label)\n",
    "\n",
    "plt.xlabel('episode')\n",
    "plt.ylabel('reward')\n",
    "plt.title('Random vs. SB3 Agents')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "p3.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
